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The Sleep Revolution project : the concept and objectives

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dc.contributor Landspitali - The National University Hospital of Iceland
dc.contributor.author Sleep Revolution
dc.date.accessioned 2022-09-29T01:02:52Z
dc.date.available 2022-09-29T01:02:52Z
dc.date.issued 2022-06-30
dc.identifier.citation Sleep Revolution 2022 , ' The Sleep Revolution project : the concept and objectives ' , Journal of Sleep Research , vol. 31 , no. 4 , e13630 , pp. e13630 . https://doi.org/10.1111/jsr.13630
dc.identifier.issn 0962-1105
dc.identifier.other PURE: 60377053
dc.identifier.other PURE UUID: f58d9d61-e041-4fa0-b5fc-67c34dd18618
dc.identifier.other Scopus: 85133791961
dc.identifier.other unpaywall: 10.1111/jsr.13630
dc.identifier.uri https://hdl.handle.net/20.500.11815/3493
dc.description Funding Information: This work is supported by the European Union's Horizon 2020 Research and Innovation Programme under Grant 965417. Publisher Copyright: © 2022 European Sleep Research Society. © 2022 European Sleep Research Society.
dc.description.abstract Obstructive sleep apnea is linked to severe health consequences such as hypertension, daytime sleepiness, and cardiovascular disease. Nearly a billion people are estimated to have obstructive sleep apnea with a substantial economic burden. However, the current diagnostic parameter of obstructive sleep apnea, the apnea–hypopnea index, correlates poorly with related comorbidities and symptoms. Obstructive sleep apnea severity is measured by counting respiratory events, while other physiologically relevant consequences are ignored. Furthermore, as the clinical methods for analysing polysomnographic signals are outdated, laborious, and expensive, most patients with obstructive sleep apnea remain undiagnosed. Therefore, more personalised diagnostic approaches are urgently needed. The Sleep Revolution, funded by the European Union's Horizon 2020 Research and Innovation Programme, aims to tackle these shortcomings by developing machine learning tools to better estimate obstructive sleep apnea severity and phenotypes. This allows for improved personalised treatment options, including increased patient participation. Also, implementing these tools will alleviate the costs and increase the availability of sleep studies by decreasing manual scoring labour. Finally, the project aims to design a digital platform that functions as a bridge between researchers, patients, and clinicians, with an electronic sleep diary, objective cognitive tests, and questionnaires in a mobile application. These ambitious goals will be achieved through extensive collaboration between 39 centres, including expertise from sleep medicine, computer science, and industry and by utilising tens of thousands of retrospectively and prospectively collected sleep recordings. With the commitment of the European Sleep Research Society and Assembly of National Sleep Societies, the Sleep Revolution has the unique possibility to create new standardised guidelines for sleep medicine.
dc.format.extent e13630
dc.language.iso en
dc.relation.ispartofseries Journal of Sleep Research; 31(4)
dc.rights info:eu-repo/semantics/openAccess
dc.subject Náttúrufræðingar
dc.subject apnea–hypopnea index
dc.subject costs
dc.subject digital management platform
dc.subject e-health
dc.subject exercise
dc.subject lifestyles
dc.subject machine learning
dc.subject mobile application
dc.subject neurocognitive tests
dc.subject P4 medicine
dc.subject participatory
dc.subject patient-reported outcome measures
dc.subject polysomnography
dc.subject self-applied home testing
dc.subject sleep diary
dc.subject sleep revolution
dc.subject telemedicine
dc.subject Humans
dc.subject Sleep Apnea, Obstructive/diagnosis
dc.subject Polysomnography
dc.subject Disorders of Excessive Somnolence
dc.subject Sleep
dc.subject Retrospective Studies
dc.subject Cognitive Neuroscience
dc.subject Behavioral Neuroscience
dc.title The Sleep Revolution project : the concept and objectives
dc.type /dk/atira/pure/researchoutput/researchoutputtypes/contributiontojournal/systematicreview
dc.description.version Peer reviewed
dc.identifier.pmid 35770626
dc.identifier.doi https://doi.org/10.1111/jsr.13630
dc.relation.url http://www.scopus.com/inward/record.url?scp=85133791961&partnerID=8YFLogxK
dc.contributor.department Department of Engineering
dc.contributor.department Department of Computer Science
dc.contributor.department Department of Psychology

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